




from random import randint, random






def    snake_case(							__magic_name__					,						__magic_name__					,						__magic_name__					,						__magic_name__ = False					,						__magic_name__ = False					,						__magic_name__ = 5					,						)					->				Tuple:





     '''simple docstring'''


     lowercase   :  Tuple        =					[[-1] * number_of_cells]  # Create a highway without any car
     lowercase   :  Optional[Any]        =					0
     lowercase   :  Dict        =					max(a_					,						0       )
     while i < number_of_cells:
          lowercase   :  List[Any]        =					(
              randint(0					,						a_       ) if random_speed else initial_speed
          )  # Place the cars
          i += (
              randint(1					,						max_speed * 2       ) if random_frequency else frequency
          )  # Arbitrary number, may need tuning
     return highway






def    snake_case(							__magic_name__					,						__magic_name__       )					->				Tuple:





     '''simple docstring'''


     lowercase   :  Union[str, Any]        =					0
     lowercase   :  Union[str, Any]        =					highway_now[car_index + 1 :]
     for cell in range(len(a_       )       ):  # May need a better name for this
          if cells[cell] != -1:  # If the cell is not empty then
               return distance  # we have the distance we wanted
          distance += 1
     # Here if the car is near the end of the highway
     return distance + get_distance(a_					,						-1       )






def    snake_case(							__magic_name__					,						__magic_name__					,						__magic_name__       )					->				int:





     '''simple docstring'''


     lowercase   :  str        =					len(a_       )
     # Beforce calculations, the highway is empty
     lowercase   :  Optional[int]        =					[-1] * number_of_cells

     for car_index in range(a_       ):
          if highway_now[car_index] != -1:
               # Add 1 to the current speed of the car and cap the speed
               lowercase   :  int        =					min(highway_now[car_index] + 1					,						a_       )
               # Number of empty cell before the next car
               lowercase   :  Optional[int]        =					get_distance(a_					,						a_       ) - 1
               # We can't have the car causing an accident
               lowercase   :  int        =					min(next_highway[car_index]					,						a_       )
               if random() < probability:
                    # Randomly, a driver will slow down
                    lowercase   :  Tuple        =					max(next_highway[car_index] - 1					,						0       )
     return next_highway






def    snake_case(							__magic_name__					,						__magic_name__					,						__magic_name__					,						__magic_name__       )					->				Union[str, Any]:





     '''simple docstring'''


     lowercase   :  Union[str, Any]        =					len(highway[0]       )

     for i in range(a_       ):
          lowercase   :  Union[str, Any]        =					update(highway[i]					,						a_					,						a_       )
          lowercase   :  Optional[Any]        =					[-1] * number_of_cells

          for car_index in range(a_       ):
               lowercase   :  Tuple        =					next_speeds_calculated[car_index]
               if speed != -1:
                    # Change the position based on the speed (with % to create the loop)
                    lowercase   :  Optional[int]        =					(car_index + speed) % number_of_cells
                    # Commit the change of position
                    lowercase   :  Tuple        =					speed
          highway.append(a_       )

     return highway


if __name__ == "__main__":
 import doctest

 doctest.testmod()





'''simple docstring'''


from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_					=   {
    "configuration_trajectory_transformer": [
        "TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "TrajectoryTransformerConfig",
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_					=   [
      "TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TrajectoryTransformerModel",
      "TrajectoryTransformerPreTrainedModel",
      "load_tf_weights_in_trajectory_transformer",
  ]


if TYPE_CHECKING:
  from .configuration_trajectory_transformer import (
      TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      TrajectoryTransformerConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_trajectory_transformer import (
        TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TrajectoryTransformerModel,
        TrajectoryTransformerPreTrainedModel,
        load_tf_weights_in_trajectory_transformer,
    )


else:
  import sys

  UpperCAmelCase_					=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



